Towards shape optimization of flow channels in profile extrusion dies using reinforcement learning

Abstract

Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt is pressed to impart the desired shape. However, due to an inhomogeneous velocity distribution at the die exit or residual stresses inside the extrudate, the final shape of the manufactured part often deviates from the desired one [1]. To avoid these deviations, we want to optimize the shape of the flow channel inside the die computationally. This has already been investigated in the literature using conventional optimization approaches [2,3].

In this work, we investigate the feasibility of Reinforcement Learning (RL) as a learning-based approach for shape optimization. RL is based on trial-and-error interactions of an agent with an environment. For each action, the agent is rewarded and informed about the subsequent state of the environment, which for this application stems from a high-fidelity Finite Element Method (FEM) simulation.

We will introduce a 2D test case as a proof-of-concept, in which an RL agent learns to change the geometry of a flow channel by modifying its representation as computational mesh through a spline-based deformation method known as Free Form Deformation (FFD) [4]. We will show that an agent can be trained to optimize the geometry for different values of the quantity of interest and that the learning behavior is reproducible, which renders the RL-based approach promising for further research.